The language’s two main advantages are its simplicity and flexibility. Its straightforward syntax and use of indented spaces make it easy to learn, read and share. Its avid practitioners, known as Pythonistas, have uploaded 145,000 custom-built software packages to an online repository. These cover everything from game development to astronomy, and can be installed and inserted into a Python program in a matter of seconds.

Quantum computers might sound a bit exotic and far into the future, but in reality, they are now accessible in the cloud or through emulators for everyone to write quantum code. In this tutorial, we’ll go through how you can program a simple quantum computer to generate random numbers.
This example can be done on any emulator or quantum computer. For this blog post, the free and open source Python library ProjectQ is used.
ProjectQ can emulate a quantum computer on any CPU, or connect to IBMs quantum computer as a backend.

Test development is key for most software projects. In this post, we are going to discuss 4 different tests: unit tests, smoke tests, integration tests and utility tests. In simple words, unit tests make sure that each class or function behaves as it should, smoke tests make sure that the system works, integration tests make sure that the program results are acceptable and utility tests give an example on how to use a class or function. We will show how to work with these tests in Python.

Now, Scikit-Learn, the leading machine learning library in Python, does provide random data set generation capability for regression and classification problems. However, the user have no easy control over the underlying mechanics of the data generation and the regression output are not a definitive function of inputs?—?they are truly random. While this may be sufficient for many problems, one may often require a controllable way to generate these problems based on a well-defined function (involving linear, nonlinear, rational, or even transcendental terms).

As a lifelong Utahan, I began to wonder how bad is the pollution? The news reporters seem to think it’s pretty bad. The politicians say it’s never been better. So how bad is it? What impact does it have on things like real estate value? How many people are impacted?
As we continue our series analyzing Utah’s air quality with Randy Zwitch, Senior Developer Advocate at MapD, we now turn our focus on to cleaning the data that we received from the EPA’s Air Quality API. In addition we will cover how we use the data to calculate an Air Quality Index (AQI) score and exporting the data out for import into MapD which we will use to further analyze the data.

This tutorial will implement the genetic algorithm optimization technique in Python based on a simple example in which we are trying to maximize the output of an equation. The tutorial uses the decimal representation for genes, one point crossover, and uniform mutation.

Python 3.4.9rc1 was released on July 19th, 2018.
Python 3.4 has now entered "security fixes only" mode, and as such the only changes since Python 3.4.7 are security fixes. Also, Python 3.4.9rc1 has only been released in source code form; no more official binary installers will be produced.